AI Metrics Mastery - Measuring Success Beyond Clicks

Break free from click obsession and master AI KPIs that align with business objectives. These metrics are directly observable and measurable, but they’re also easy to understand.

Prioritize user satisfaction and trust over clicks alone. See how a better experience can lead to social sharing, amplifying content’s reach. And, easily optimize for these metrics with guided autotuning.

Getting Started With AI Metrics

The word “metric” is used in both mathematical contexts and more informal business use, and it refers to the quantitative measurement of an object’s properties. For AI, metrics measure the quality of an AI model’s output based on its intended goals.

To optimize their AI models’ performance, businesses must first define quantitative and qualitative benefits that are directly measurable. For example, a business’s top-level goals may be customer satisfaction or revenue, and these are directly measured by classic IT and business metrics such as MTTR (machine time to resolution) and MRCR (mean round trip cost per resource request).

Once these quantitative objectives are established, the next step is to set specific metrics to measure those goals. This can be done by defining, characterizing, and theoretically and empirically developing metrics, measurements, and evaluation methods. For example, the National Institute of Standards and Technology (NIST) is working on metrics and evaluation methodologies for AI-based technologies.

The performance of AI models can be characterized by various predictive types of metrics, including regression and classification. Regression measures the relationship between a variable and its output, while classification evaluates the class to which an output belongs. For example, an AI model that determines the type of disease a patient has might be categorized as a diagnosis in a hospital’s database.

An important aspect of an AI’s performance is its precision, which measures how well it distinguishes between different items. For example, a machine that can quickly and accurately sort apples from a stockpile of fruit would have high precision. The recall metric, on the other hand, focuses on guaranteeing that an AI model doesn’t overlook key information.

To help you monitor your AI’s performance, Microsoft provides AI Metrics Advisor for Azure. This solution simplifies your data stream and data monitoring lifecycle by ingesting, preprocessing, and visualizing your metrics. It also allows you to automate alerts for anomalies based on your custom criteria and parameters. AI Metrics Advisor supports metrics with multiple dimensions and aggregation functions, and it uses a hierarchical structure called a diagnostic tree to organize the results.

AI Anomaly Detector

Detecting anomalies in large data sets can help you discover inefficiencies, rare events, or the root cause of problems that could impact customer experience. Anomaly detection algorithms are also useful for uncovering business opportunities and predicting future performance.

AI Metrics Advisor simplifies this process by streamlining the data and analytics lifecycle so you can manage everything from data ingestion to model evaluation, visualization and diagnostics, and alerting from a single platform. It eliminates the need to build a user interface from scratch and offers abundant database connectors for faster, more flexible data ingestion, as well as preprocessing of your datasets to ensure consistent time-series flow.

Anomaly detection requires ML models that are trained on historical data to learn the patterns and trends of normal product behavior. These models then continuously monitor incoming data, identifying any deviation from these established patterns to surface potential issues. This helps you identify product or service issues quickly, communicate them clearly to stakeholders, and make quick adjustments.

For example, LeewayHertz uses an anomaly detection algorithm to automatically notify their customers when their credit card purchases exceed a set threshold. This allows them to quickly resolve issues and reduce customer complaints, and ultimately improve overall system reliability.

Besides enhancing adaptability, clear and consistent AI-related KPIs enable a better return on investment (ROI). They’re essential for objectively assessing model performance, aligning with overall business objectives, allowing for data-driven adjustments, facilitating transparent stakeholder communication, and demonstrating ROI.

Technical metrics such as accuracy, precision, recall, F1 score, AUC-ROC, MAE, MSE, and RMSE are used to measure the performance of classification or regression-type AI systems. However, these metrics can be misleading if the underlying assumptions are not fully understood. They can also lead to false positives and negatives, which can have significant costs in the real world.

Rather than focus on metric optimization, a healthier approach to measuring AI involves the use of a broad spectrum of tools and techniques that support a more holistic picture of how an AI solution is performing in its intended application. This includes a range of qualitative accounts, external auditing, and involving a broad group of stakeholders.

AI Alerts

As the complexities of AI and machine learning continue to expand, organizations will face challenges when it comes to deployment and optimization. These include high operational costs, resource-intensive nature, and the need for a balanced approach that includes technological innovation with human expertise and curated threat data. A sole reliance on AI will likely result in an unsustainable increase in the number of false positives and exacerbate alert fatigue, leading to a reduction in security effectiveness.

One way to tackle these problems is by reducing the number of alerts sent out, which in turn reduces the burden on engineers to monitor a large number of metrics. AI Anomaly Detector can help here by automating this monitoring process and generating an alert only when it finds a significant change in the time series for a specific metric. This enables your team to focus on more pressing issues and speed up MTTD.

It also helps to prioritize alerts and filter out irrelevant ones so that only important information is pushed to the team, and the rest is automatically ignored. The AI Alerts feature offers 30 different configurations for monitoring various dimensions and metric combinations - from conversion and traffic to website performance and technical issues.

When a change is detected, AI Alerts provides a clear explanation of the anomaly with detailed graphs, including the timeseries graph and the computed thresholds. This enables teams to quickly determine the root cause of the problem and get back to work.

AI Alerts can be customized based on the needs of individual departments. For example, alerts pointing towards technical problems are typically of interest to engineering teams, while those indicating website or campaign performance are more often relevant for the marketing department.

The artificial intelligence in AI Alerts is based on Mapp’s deep data layers, which examine more than 10,000 dimension combinations to spot changes in a given time frame and provide alerts based on the unique requirements of each user. The alerts also provide root-cause analysis for each detected issue, allowing teams to focus on fixing the problem rather than analyzing metrics.

AI Autotuning

AI models are often complex and require a long time to train. During this process, developers need to monitor their progress by testing model performance against known inputs. These metrics help AI practitioners fine-tune their systems by identifying errors, improving accuracy and quality.

AI-related KPIs often overlap with existing business and IT metrics, leveraging the same data sets. For example, mean time to repair (MTTR) and first contact resolution rate (FCRR) are two common IT KPIs that can be used to measure the performance of an AI project. However, it’s important to emphasize the distinction between direct and indirect metrics when establishing AI project goals.

Indirect metrics can include customer satisfaction or net promoter scores, but should only be used to supplement more direct AI performance metrics. The latter are more likely to be influenced by the quality of the training and model evaluation processes rather than how well a gen AI performs in production.

Getting an AI to work in production can be tricky, especially as it’s being introduced into workflows with unfamiliar processes. Metrics like system latency can provide an early warning when a model is causing delays or bottlenecks. In addition, user adoption and reusability metrics reveal how well an AI is being adopted, helping to identify any barriers to adoption.

For example, a team at Johns Hopkins University has developed a new type of auto-tune that is more accurate and transparent than existing plugins and tools. The technology uses neural networks to understand what singing in key sounds like based on training data, and then synthesizes existing recordings more naturally based on that understanding. The technology is a powerful tool for musicians, but it also raises questions about musical authenticity and challenges traditional skills.

In the same way that autotune went from controversial tool to accepted part of music production, the rise of AI in audio production could reshape how we create and consume music. In the future, AI will help artists create and perform music more efficiently than ever before—but establishing a proper balance between performance metrics and potential ethical issues is crucial to ensuring success.